Mobile Systems Research Articles

Evaluating the Dynamic Comprehensive Resilience of Urban Road Network: A Case Study of Rainstorm in Xi’an, China

Rainstorms and flooding are among the most common natural disasters, which have a number of impacts on the transport system. This reality highlights the importance of understanding resilience—the ability of a system to resist disruptions and quickly recover to operational status after damage. However, current resilience assessments often overlook transport network functions and lack dynamic spatiotemporal analysis, posing challenges for comprehensive disaster impact evaluations. This study proposes an SR-PR-FR comprehensive resilience evaluation model from three dimensions: structure resilience (SR), performance resilience (PR), and function resilience (FR). Moreover, a simulation model based on Geographic Information System (GIS) and Simulation of Urban MObility (SUMO) is developed to analyze the dynamic spatial–temporal effects of a rainstorm on traffic during Xi’an’s evening rush hour. The results reveal that the southwest part of Xi’an is most prone to being congested and slower to recover, while downtown flooding is the deepest, severely affecting emergency services’ efficiency. In addition, the road network resilience returns to 70% of the normal values only before the morning rush the next day. These research results are presented across both temporal and spatial dimensions, which can help managers propose more targeted recommendations for strengthening urban risk management.

Adaptive third-order integral sliding mode control for the mobile petroleum crane-form pipelines system with input bounded

The mobile petroleum crane-form pipelines (MPCFP) system is one of the most important mechanical equipment in the petrochemical industry, consisting of rigid pipelines and rotating elbows, mainly used for loading and unloading petrochemical fluids. This paper proposes a novel neural network adaptive third-order integral sliding mode tracking control strategy with bounded inputs to achieve automatic alignment between the MPCFP system and the inlet of the tank truck. The proposed strategy replaces normal system inputs with the time derivative of the control torque. Although there are discontinuous terms in the derivative of the control torque, the integrated control torque is continuous. This unique feature ensures finite-time convergence of system state errors and eliminates sliding mode chattering. To address the issues of bounded input and modeling error, a state compensation method is introduced, and a neural network adaptive algorithm is utilized to estimate the modeling error. The stability proof process employs the Lyapunov function method, demonstrating that the system state error can converge to zero. Numerous simulation experiments validate the effectiveness of this control strategy.

Determining Validity and Reliability of an In-Field Performance Analysis System for Swimming

To permit the collection of quantitative data on start, turn and clean swimming performances in any swimming pool, the aims of the present study were to (1) validate a mobile in-field performance analysis system (PAS) against the Kistler starting block equipped with force plates and synchronized to a 2D camera system (KiSwim, Kistler, Winterthur, Switzerland), (2) assess the PAS’s interrater reliability and (3) provide percentiles as reference values for elite junior and adult swimmers. Members of the Swiss junior and adult national swimming teams including medalists at Olympic Games, World and European Championships volunteered for the present study (n = 47; age: 17 ± 4 [range: 13–29] years; World Aquatics Points: 747 ± 100 [range: 527–994]). All start and turn trials were video-recorded and analyzed using two methods: PAS and KiSwim. The PAS involves one fixed view camera recording overwater start footage and a sport action camera that is moved underwater along the side of the pool perpendicular to the swimming lane on a 1.55 m long monostand. From a total of 25 parameters determined with the PAS, 16 are also measurable with the KiSwim, of which 7 parameters showed satisfactory validity (r = 0.95–1.00, p < 0.001, %-difference < 1%). Interrater reliability was determined for all 25 parameters of the PAS and reliability was accepted for 21 of those start, turn and swimming parameters (ICC = 0.78–1.00). The percentiles for all valid and reliable parameters provide reference values for assessment of start, turn and swimming performance for junior and adult national team swimmers. The in-field PAS provides a mobile method to assess start, turn and clean swimming performance with high validity and reliability. The analysis template and manual included in the present article aid the practical application of the PAS in research and development projects as well as academic works.

Unobstructive and safe-to-wear watt-level wireless charger

A wearable microgrid that centralizes and distributes harvested energy across different body regions can optimize power utilization and reduce overall battery weight. This setup underscores the importance of developing cable-free wireless power transfer (WPT) systems for mobile and portable devices to eliminate the risks posed by wired connections, especially in dynamic and hazardous environments. We introduce a thin, stretchable, and safe hand band capable of watt-level wireless charging through the widely adopted Qi protocol operating at 130 kHz. The implementation of non-adhesive fabric encapsulation serves to protect the 50-μm-thin spiral copper antenna from mechanical strain, ensuring an overall hand band stretchability of 50%. We also create a stretchable “Ferrofabric”, characterized by a magnetic permeability of 11.3 and a tensile modulus of 75.3 kPa, that provides magnetic shielding for the antenna without compromising wearability. The “Ferrofabric” improves the coil inductance but induces core loss in AC application. By fully understanding and managing loss mechanisms such as the skin effect, proximity effect, core loss, and joule heating, we achieve a wireless charging efficiency of 71% and power delivery of 3.81 W in the kHz frequency range. Our WPT hand band is unobstructive to hand motion and can charge a handheld smartphone as fast as a desktop charger or power a battery-free chest-laminated e-tattoo sensor, with well-managed thermal and electromagnetic safety. Through a holistic electromagnetic, structural, and thermal design, our device culminates in a safe, rugged, and versatile solution for wearable WPT systems.

Fuzzy task assignment in heterogeneous distributed multi-robot system

This study addresses the problem of coordination in cooperative multi-robot systems performing complex tasks. An analysis of cooperative behavior in mobile multi-robot systems in terms of task execution accuracy by heterogeneous robots is carried out. In addition, we evaluate the capacity and compatibility of tasks assigned to robots to optimize task execution without using direct communication with the robots or a central decision-making unit. A model for task selection in heterogeneous distributed multi-robot systems is proposed. It is based on two processes: the first decomposes complex tasks into elementary tasks, and the second assigns elementary tasks to mobile robots for real-time execution. The distribution of elementary tasks is NP-hard, which leads us to recommend approximate solutions. A fuzzy system called Fuzzy Decision Making in Task Selection is proposed, which uses fuzzy logic to solve this problem. This system allows robots to choose to perform any task in the future. An approach is presented that uses two cascading fuzzy systems. The first calculates the utility of the robot and then activates the second fuzzy system to calculate the utility of the task. By using the output of the fuzzy decision system in our model, each robot will be able to decide for itself which tasks to perform. The results of a simulation of mobile robots transporting goods demonstrate the effectiveness of this fuzzy decision-maker.

What promotes the mobile payment behavior of the elderly?

Mobile payment, as a new form of transactional payment method, has garnered attention from users. However, few studies have discussed the mobile payment behavior of the elderly. Our research aims to explore the influencing factors on elderly’s intention to use mobile payments. This research established and empirically validated a mobile payment acceptance model for the elderly. Structural equation modeling was employed to analyze data from 316 elderly participants. The results indicate that perceived ease of use directly affects perceived usefulness. The Mobile payment intention of the elderly is positive influenced by perceived usefulness and perceived ease to use. Furthermore, information quality and service quality directly influence perceived trust. Perceived trust, information quality, and service quality all have directly affects on the intention to use mobile payments. Mobile payment intention is negatively affected by perceived risk. The findings of this study can serve as references for mobile payment developers, as well as relevant policy-making and regulatory authorities, facilitating the design of mobile payment technology systems that are more suitable for the elderly and promoting the sustainable development of mobile payments.

A Comprehensive Review of Mobile Robot Navigation Using Deep Reinforcement Learning Algorithms in Crowded Environments

Navigation is a crucial challenge for mobile robots. Currently, deep reinforcement learning has attracted considerable attention and has witnessed substantial development owing to its robust performance and learning capabilities in real-world scenarios. Scientists leverage the advantages of deep neural networks, such as long short-term memory, recurrent neural networks, and convolutional neural networks, to integrate them into mobile robot navigation based on deep reinforcement learning. This integration aims to enhance the robot's motion control performance in both static and dynamic environments. This paper illustrates a comprehensive survey of deep reinforcement learning methods applied to mobile robot navigation systems in crowded environments, exploring various navigation frameworks based on deep reinforcement learning and their benefits over traditional simultaneous localization and mapping-based frameworks. Subsequently, we comprehensively compare and analyze the relationships and differences among three types of navigation: autonomous-based navigation, navigation based on simultaneous localization and mapping, and planning-based navigation. Moreover, the crowded environment includes static, dynamic, and a combination of obstacles in different typical application scenarios. Finally, we offer insights into the evolution of navigation based on deep reinforcement learning, addressing the problems and providing potential solutions associated with this emerging field.

Experimental assessment of IMU data processing techniques for a backpack mobile laser scanning system

Abstract. Positioning techniques are fundamental in many automation tasks with several applications. In GNSS-denied environments like in dense forests, other alternatives are required, such as inertial and visual navigation. However, Inertial Measurement Units (IMUs) data, mainly those from microelectromechanical-system (MEMS), are noisy, which affects the orientation estimation. MEMS IMUs have been employed in mobile laser scanning systems due to their compact design and low-cost solutions for short-term navigation. In this paper, we have compared three IMU processing techniques freely available: MAH (Mahony et al., 2009), MAD (Madgwick et al., 2011) and DCM (Hyyti and Visala, 2015). These techniques implemented different approaches to estimate the attitude. They were experimentally assessed with data from a backpack mobile laser scanning system, which is composed of an OS0-128 Ouster LiDAR equipped with an internal IMU. We have used data from a 5-second trajectory segment aiming to evaluate the attitude and position estimation for a local path. The results showed that the DCM algorithm maintained a consistent velocity for 5 seconds, achieving a positional error of 1.4 m, 0.06 m, and 1.05 m along the X-, Y- and Z-axis, respectively. In contrast, MAD and MAH showed a position error over 20 m, 7 m and 3 m along the X-, Y- and Z-axis, respectively, which was affected by the velocity drift.

Feasibility analysis of e‐bus‐based transit system for sustainable urban mobility

Feasibility analysis of e‐bus‐based transit system for sustainable urban mobility

An integrated model of electric bus energy consumption and optimised depot charging

The electrification of buses, and the depots servicing them, is a high priority for sustainability but is hindered by challenges at the intersection of the mobility and electricity systems, such as trade-offs of charging infrastructure and scheduling with the stress placed on the grid. This paper presents a model for studying and co-optimise these variables. The model has two standout features. Firstly, that it integrates a data-driven model for predicting the energy consumption of electric buses based on route details with a bus depot charging optimisation algorithm that minimises the loading on the grid. Secondly, it is compatible with the publicly available route and schedule data published by bus operators and is available open-source for extension. The results foreground the impact of route topology, temperature, and traffic congestion on energy consumption, and the interdependencies of these with timetabling, charging infrastructure and battery designs in driving grid loading.

Optimal expansion planning of a self-healing distribution system considering resiliency investment alternatives

This paper proposes a three-stage optimization framework for the expansion planning of a self-healing distribution system that determines the optimal characteristics of distributed generation, energy storage systems, electric vehicle charging stations, and sectionalizing switches for the planning horizon. The main contribution of this model is that the proposed model considers the resilient investment alternatives in the expansion planning exercise to reduce the system's vulnerability against external shocks. The mobile energy storage system commitment in contingent conditions is another contribution of this paper. In the first stage, the optimal location, capacity, and time of installation of the electricity facilities are calculated. Then, the optimal allocation of sectionalizing switches is performed in the second stage. The third stage consists of three levels. In the first and second levels, the optimal normal and contingent operational scheduling are determined, respectively. The system is sectionalized into multi-microgrid systems in contingent conditions. Finally, the resilient investment alternatives for the designed system are evaluated. The proposed model utilizes a self-healing index and resilient expansion planning index to assess the impacts of resilient investment alternatives on the operational scheduling conditions. The proposed model was evaluated using the IEEE 123-bus system. The proposed method reduced the estimated average value of the worst-case energy not supplied by 50.52 % for the 5th year of the planning horizon concerning the no-resiliency investment case. Further, the proposed resilience investment method increased the self-healing index by about 9.32 % concerning the no-resiliency investment case.

An Exploratory Study of Small-Group Learning Interactions in Pre-Clerkship Medical Education: Uncovering a Mismatch Between Student Perceptions and Real-Time Observations

Small-group learning is a mainstay of medical education, and group functioning can have a major influence on these learning experiences. Our objective was to explore verbal exchange patterns within small-group learning sessions and examine how different patterns related to tutor involvement, tutor expertise, and participants’ perceptions. A non-participant observer collected group interactivity data using a real-time mobile device-based system. Verbal interaction patterns were visualized and analyzed using social network analysis and correlated with participant survey data and aggregate course grades. There were 46 observations across 30 separate groups. Group interactions clustered into four patterns defined by (1) tutor involvement (high vs. low) and (2) interactivity (high vs. low). Interaction patterns were largely stable for a given group and groups with content expert facilitators were generally less interactive. Students reported objectively fewer interactive groups as more interactive and enjoyable. There were no significant intergroup differences in aggregate course grades. Paradoxically, student perceptions were not aligned with observed interactivity data, and tutor content expertise influenced group interactivity. These findings suggest the need to better manage learner expectations of small-group learning, and to explicitly reflect on and develop skills for effective collaborative learning with both faculty and students.

Complete Visibility Algorithms of Luminous Robots With Two‐Color Lights on Grid

ABSTRACTAn autonomous mobile robot system is a distributed system consisting of multiple mobile computational entities, called robots, which autonomously and repeatedly perform three fundamental operations: look, compute, and move. Various challenges in such systems, including gathering, pattern formation, and flocking, have been extensively studied to explore the relationship between the robots' capabilities and the feasibility of solving these problems (i.e., solvability). In this study, we focus on the complete visibility problem, which aims to relocate all robots on an infinite grid plane so that every robot is visible to every other robot (i.e., complete visibility). We assume that each robot is a luminous robot (i.e., has a light with a constant number of colors) and opaque (non‐transparent). This paper primarily examines the number of light colors required for each robot to solve the complete visibility problem. Specifically, we investigate the question: “how many colors can we reduce while still achieving complete visibility?” (even under certain stronger assumptions). As an answer to the above question, we show the existence of a deterministic algorithm to achieve complete visibility (i.e., every robot can observe all the other robots) using only two colors of light, if the robots agree on the directions and orientations of both axes. The proposed algorithm correctly works even if robots operate asynchronously and have no knowledge of the total number of robots. Moreover, its spatial complexity (i.e., the area of the smallest enclosed rectangle that includes all robots in the final configuration) ensures the optimal one, , where is the number of robots.

Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing

Despite the promising benefits that ride-sharing offers, there has been a lack of research on the benefits of high-capacity ride-sharing services. Prior research has also overlooked the relationship between traffic volume and the degree of traffic congestion and emissions. To address these gaps, this study develops an open-source agent-based simulation platform and a heuristic algorithm to quantify the benefits of high-capacity ride-sharing with significantly lower computational costs. The simulation platform integrates a traffic emission model and a speed-density traffic flow model to characterise the interactions between traffic congestion levels and emissions. The experiment results demonstrate that ride-sharing with vehicle capacities of 2, 4, and 6 passengers can alleviate total traffic congestion by approximately 3%, 4%, and 5%, and reduce traffic emissions of a ride-sourcing system by approximately 30%, 45%, and 50%, respectively. This study can guide transportation network companies in designing and managing more efficient and environment-friendly mobility systems.

Intraoperative Invasive Coronary Angiography after Coronary Artery Bypass Grafting.

The aim of this study was to prospectively evaluate the feasibility and safety of intraoperative invasive coronary angiography (ICA) following coronary artery bypass grafting using a mobile angiography C-arm. Between August 2020 and December 2021, 18 patients were enrolled for intraoperative ICA following coronary artery bypass grafting. After skin closure, ICA was performed including angiography of all established bypass grafts via a mobile angiography system by an interventional cardiologist. Data on graft patency, stenosis, and kinking were assessed. Grafts were rated on an ordinal scale ranging from very poor (1) to excellent (5). Furthermore, the impact of ICA compared with flow measurement was assessed using the ordinal Likert scale ranging from (I) worse to (V) much better. The ICA was considered better (V) compared with transient flow measurement in 38 (93%) and comparable (III) in 3 (7%) distal anastomoses. ICA impacted clinical or surgical decision-making in three patients (17%). In one patient, dual antiplatelet therapy for 6 months was initiated and rethoracotomy was needed in two (11%) patients with bypass graft revision and additional bypass grafting for graft occlusion. There were no cerebral and distal embolic events or access vessel complications observed and no postoperative acute kidney injury occurred. Intraoperative angiography after coronary bypass grafting is safe. Using a mobile angiographic device, graft patency, and function assessment was superior to transit time flow measurement leading to further consequences in a relevant number of patients. Therefore, it has the potential to reduce postoperative myocardial injury and improve survival.

Mitigating the Charging Rush Hour

Great effort is put into making our mobility system more sustainable in order to mitigate climate change. One corner stone of this endeavour is the transition from internal combustion engines to electric engines for private cars. This transition, however, introduces new challenges, especially regarding the demand for electrical energy from renewable sources. One emerging phenomenon is the so-called charging rush hour, i.e. a sharp demand spike when many people arrive home and begin charging their electric cars. In this study we use an agent-based model calibrated with empirical data on mobility behaviour to investigate strategies for mitigating this charging rush hour. Studied counter strategies include telecommuting and the possibility to charge the car at work. Our findings show that the baseline peak of 65 MW per 100 000 people can only be reduced to 55 MW per 100 000 people even when combining multiple strategies. Thus the small incentives and policy changes investigated here are not enough to solve the problem of the charging rush hour and more disruptive changes to our mobility system are required.

Simultaneous determination of 17 perfluoroalkyl substances in beef by EMR-Lipid dispersive solid-phase extraction and ultra performance liquid chromatography-tandem mass spectrometry

The ability to accurately analyze perfluoroalkyl substance (PFAS) levels in beef is imperative in order to effectively assess food-safety risks and ensure consumer safety because PFASs are harmful and prevalent in beef. In this study, we developed a rapid and accurate method for the simultaneously determination of the 17 PFASs in beef using dispersive solid-phase extraction (d-SPE) and ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), and optimized the mobile phase system, extraction solvent, and d-SPE materials. Samples were finally extracted using 0.1% (v/v) formic acid in acetonitrile, cleaned using d-SPE with PSA, C18, GCB, and EMR-Lipid, separated using an Acquity Premier BEH C18 column (100 mm×2.1 mm, 1.7 μm) with 0.5 mmol/L ammonium fluoride aqueous solution and methanol as the mobile phases at a flow rate of 0.3 mL/min. Analytes were detected in negative ion switching mode (ESI-) with multiple reaction monitoring (MRM) scanning, and quantitatively analyzed using the internal standard method. The 17 PFASs exhibited linearity in the 0.2-20.0 μg/L range under the optimal experimental conditions, with correlation coefficients of 0.9915-0.9999. The method delivered limits of detection (LODs) of 0.003-0.007 μg/kg and limits of quantification (LOQs) of 0.01-0.02 μg/kg. The 17 PFASs exhibited recoveries of 71.1%-127.4% with RSDs of 0.6%-14.4% when spiked at three levels (0.05, 0.5, and 1.8 μg/kg). We optimized the mobile phase system, which revealed that, compared with 2.0, 5.0, and 10.0 mmol/L ammonium formate or ammonium acetate in aqueous methanol, 0.5 mmol/L ammonium fluoride in aqueous methanol exhibited higher sensitivities for all the 17 PFASs, with PFASs bearing long-chain carboxylic acids (C10-C18) showing 1-2 fold increases in sensitivity. PFASs do not dissociate in acidic environments, favoring their entry into the organic phase. Therefore, we investigated the effect of extractant acidity, which revealed that the 17 PFASs were better extracted using 0.1% (v/v) formic acid in acetonitrile. The beef matrix has a complex composition; consequently, d-SPE adsorbents were required to purify samples and reduce matrix effects. The purification effects of four adsorbents (PSA, C18, GCB, and EMR-Lipid) toward the 17 PFASs and the amount of EMR-Lipid used were investigated, which revealed that 100 mg PSA+80 mg C18+40 mg GCB+150 mg EMR-Lipid exhibited superior matrix-purification behavior. We also investigated the effects of various injection solutions and types of syringe filter, with pure methanol selected for reconstitution and high-speed supernatant centrifugation applied prior to injection. The developed method is simple, rapid, sensitive, and reproducible, and can be used to simultaneously, rapidly, and accurately determine various perfluoroalkyl compounds in beef.

Competitive cost-effective memory access predictor through short-term online SVM and dynamic vocabularies

In recent years, there has been a significant increase in the processing of massive amounts of data, driven by the growing demands of mobile systems, parallel and distributed architectures, and real-time systems. This applies to various types of platforms, both specific and general-purpose. Despite numerous advancements in Computer Systems, a critical challenge remains: the efficiency and speed of memory access. This bottleneck is being addressed through cache prefetching, that is, by predicting the next memory address to be accessed and then by having always prefetched in the cache system those data to be used shortly by the processor. This paper explores established intelligent techniques for address prediction, examining their limitations and analyzing the memory access patterns of popular software applications. Building on the successes of previous intelligent predictors based on Machine and Deep Learning models, we introduce a new predictor, SVM4AP (Support Vector Machine For Address Prediction), designed to overcome the identified drawbacks of its predecessors. The architecture of SVM4AP improves the trade-off between performance and cost, compared to those previous proposals in the literature, achieving high accuracy through short-term learning. Comparisons are made with two prominent predictors from the literature: the classical DFCM (Differential Finite Context Method) and the contemporary Deep Learning-based DCLSTM (Doubly Compressed Long-Short Term Memory). The results demonstrate that SVM4AP achieves superior cost-effectiveness across various configurations. Simulations reveal that SVM4AP configurations dominate both DFCM and DCLSTM counterparts, forming the majority of the first Paretto front. Particularly noteworthy is the significant advantage of our proposal for small-size predictors. Furthermore, we release an open-source tool enabling the scientific community to reproduce the results presented in this paper using a set of benchmark traces.

Analysis and Prediction of Path Loss for Mobile Communication Lines in Nasarawa State Using Propagation Models

The design of future-generation mobile communication systems depends critically on the path loss prediction methods and their suitability to various signal propagation regions. Even though 5G has been launched in Nigeria, its deployment still faces significant challenges especially in the suburban, and non-urban regions which still depends on 4G. Many operators are still in the process of upgrading their existing 4G networks to support network coverage and higher speed data throughput. Due to the unique mix of Nasarawa State with diverse terrains, in this study, path loss prediction for the key telecommunication networks in Nasarawa State was carried out using Signal Strength Info App. Data was collected for a period of eight weeks (August, 2nd to September, 4th 2023) focusing on 4G LTE networks operating within the 1800 MHz frequency band. Analysis of path loss using empirical models such as Hata, Egli, and COST-231 Hata models were compared with the measured path loss in different terrains of urban, sub-urban, and non-urban environments. The Root Mean Square Error (RMSE) analysis was carried out between empirical and measured path losses. Results shows that, the order of mean predicted path loss at 5km was free space model (BN>CN>DN.AN), Hata model (DN>BN>CN>AN), Cos-231 Hata model (CN>AN>BN>DN), and Egli model (AN>BN>DN>CN). Hate model predicted the highest path loss values while Egli model predicted the lowest values. Overall, Egli model is found to be the most reliable and suitable path loss prediction model for entire Nasarawa State with RMSE value 5.99dBm, 2.90dBm and 3.14dBm for Nasarawa, Keffi and Akwanga LGA respectively. However, Cost-231 Hata model with RMSE value of 5.71dBm is suitable for path loss prediction in Karu due to its irregular terrain. The findings of this study are significant for practical applications in network planning, base station coverage, frequency allocation, and signal strength management, ensuring optimal mobile network performance across various terrains.

Korean policy strategies for autonomous vehicles in platform-based mobility systems

ABSTRACT This study presents a comprehensive analysis of the economic and policy dimensions crucial for the integration of autonomous vehicles within platform-based mobility systems. Utilizing the Mini-Delphi method, we gathered expert insights to form strategic policy recommendations that address the challenges and opportunities in the autonomous vehicle sector. The research underscores the potential economic impacts of autonomous mobility services and suggests robust policy frameworks to support the sustainable growth of platform-based logistics and transportation services. Our findings aim to guide policymakers and industry stakeholders in navigating the complex landscape of digital transformation in mobility.